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Abstract:

Systems and methods for reducing the effects of motion on functional
optical coherence tomography (OCT) imaging are described. Embodiments
including post-processing and motion tracking are presented. A preferred
embodiment in which functional OCT data is collected and analyzed for
motion as a multiple scan unit is described. An extension of the
invention to the collection of large field of view or montaged functional
OCT data sets is also presented.

Claims:

1. A method of collecting and analyzing functional OCT imaging data of an
eye of a patient, said method comprising: acquiring a plurality of
measurement data from the eye of a patient; monitoring the eye position
to detect transverse motion while acquiring the plurality of measurement
data, and wherein the eye position information is used to ensure that at
least two measurements are acquired from the same position; measuring the
difference between the at least two measurements; storing or displaying
the difference.

2. A method as recited in claim 1, wherein measurement data is rejected
if eye motion has caused it to be displaced relative to another
measurement.

3. A method as recited in claim 1, wherein measurement data from the same
location are re-acquired if the eye position monitoring detects motion
during the acquisition

4. A method as recited in claim 1, wherein the direction of the scanning
device is corrected based on the monitored eye position to acquire data
at the same location.

5. A method as recited in claim 1, wherein the two measurements are made
sequentially with no motion correction being applied between them.

6. A method as recited in claim 1, wherein the measurement data are
A-scans.

7. A method as recited in claim 1, wherein the measurement data are
B-scans.

8. A method of collecting and analyzing functional OCT imaging data of
the retina of the eye, said method comprising: acquiring a set of OCT
data of the retina comprising a plurality of cluster scans, wherein each
cluster scan comprises at least two measurements covering approximately
the same transverse locations on the retina; monitoring the eye position
to detect transverse motion while acquiring each cluster scan, and
wherein the measurements associated with at least one cluster scan are
rejected if eye motion has occurred during the acquisition of said
cluster scan; analyzing the remaining measurement data to determine
motion contrast; and displaying an image illustrating the motion
contrast.

9. A method as recited in claim 8, wherein a cluster scan is re-acquired
if the eye position monitoring detects motion during the acquisition.

10. A method as recited in claim 8, wherein the eye position monitoring
is accomplished by a fundus imaging based tracking system.

11. A method as recited in claim 11, wherein the update rate of the
imaging system is synchronized to the time required to acquire a single
cluster scan.

12. A method of collecting and analyzing large field of view functional
OCT imaging data of an eye of a patient, said method comprising:
acquiring a large field of view data set of the eye comprising at least
two smaller data sets, wherein each data set is centered on a different
location within the eye; monitoring the eye position while acquiring the
data sets, and wherein the location of each data set relative to the
other is known; analyzing the multiple data sets to determine motion
contrast; and displaying an image of the motion contrast.

13. A method as recited in claim 12, wherein the eye position monitoring
accounts for transverse motion during data acquisition.

14. A method as recited in claim 12, wherein the eye position monitoring
accounts for tilt changes occurring during data acquisition.

15. A method as recited in claim 12, wherein the eye position monitoring
accounts for angle changes during data acquisition.

16. A method as recited in claim 12, wherein the displayed image is a
composite en face vasculature image.

17. A method as recited in claim 12, wherein the patient is allowed to
sit back from the instrument between acquisition of the at least two data
sets. (needs support in specification??)

18. A method as recited in claim 12, wherein the data acquisition is
initiated by a single interaction with a user interface and the system
automatically acquires the multiple data sets without requiring further
interaction with the user interface.

19. An OCT system for collecting and analyzing functional OCT image data,
said system comprising: an OCT measurement system for acquiring a
plurality of measurements from the eye of a patient; a tracking system
for monitoring the location of the eye during the OCT measurement
acquisition; a processor for analyzing the tracking information and for
controlling the measurement system to ensure that at least two OCT
measurements are acquired from the same position on the eye, said
processor for determining the difference between the at least two
measurements to highlight motion contrast information; and a display for
displaying an image of the motion contrast information.

20. An OCT system as recited in claim 19, wherein the tracking system is
a fundus imaging system.

21. An OCT system as recited in claim 19, wherein the processor instructs
the OCT measurement system to re-acquire a measurement if motion
exceeding a pre-defined threshold is determined.

Description:

PRIORITY

[0001] This application claims priority to U.S. Provisional Application
Ser. No. 61/505,483 filed Jul. 7, 2011, and U.S. Provisional Application
Ser. No. 61/645,464 filed May 10, 2012, both of which are hereby
incorporated by reference.

TECHNICAL FIELD

[0002] One or more embodiments of the present invention relate to the
field of Optical Coherence Tomography (OCT). In particular, the invention
described herein provides systems and methods for achieving higher
quality and larger field of view functional OCT images.

BACKGROUND

[0003] Optical coherence tomography (OCT) is a noninvasive, noncontact
imaging modality that uses coherence gating to obtain high-resolution
cross-sectional images of tissue microstructure. Several implementations
of OCT have been developed. In Frequency domain OCT (FD-OCT), the
interferometric signal between light from a reference and the
back-scattered light from a sample point is recorded in the frequency
domain either by using a dispersive spectrometer in the detection arm in
the case of spectral-domain OCT (SD-OCT) or rapidly tuning a swept laser
source in the case of swept-source OCT (SS-OCT). After a wavelength
calibration, a one-dimensional Fourier transform is taken to obtain an
A-line spatial distribution of the object scattering potential.

[0004] Functional OCT can provide important clinical information that is
not available in the typical intensity based structural OCT images. There
have been several functional contrast enhancement methods including
Doppler OCT, Phase-sensitive OCT measurements, Polarization Sensitive
OCT, Spectroscopic OCT, etc. Integration of functional extensions can
greatly enhance the capabilities of OCT for a range of applications in
medicine.

[0005] One of the most promising functional extensions of OCT has been the
field of OCT angiography which is based on flow contrast. Visualization
of the detailed vasculature using OCT could enable doctors to obtain new
and useful clinical information for diagnosis and management of eye
diseases in a non-invasive manner. Fluorescein angiography and
indocyanine green (ICG) angiography are currently the gold standards for
vasculature visualization in the eye. However, the invasiveness of these
approaches combined with possible complications (allergy to dyes, side
effects) make them unsuitable techniques for widespread screening
applications in ophthalmic clinics. There are several flow contrast
techniques in OCT imaging that utilize the change in data between
successive B-scans or frames (inter-frame change analysis) of the OCT
intensity or phase-resolved OCT data. One of the major applications of
such techniques has been to generate en face vasculature images of the
retina. High resolution en face visualization based on inter-frame change
analysis requires high density of sampling points and hence the time
required to finish such scans can be up to an order of magnitude higher
compared to regular cube scans used in commercial OCT systems.

[0006] While OCT angiography appears to be an exciting technology, there
are several technical limitations that need to be overcome before it can
gain widespread acceptance in clinical settings. Typically, the most
common approach for determining motion contrast is to obtain multiple
B-scans (at the same location or closely spaced) and analyze the change
in OCT data due to motion. One of the major limitations of OCT
angiography is the long acquisition times and associated motion artifacts
that can affect analysis. Eye motion can result in loss of data, image
artifacts and hence greatly reduces the usability of the acquired data.
While axial motion can be detected and compensated for, it is relatively
difficult and time consuming to detect all cases of transverse motion
using post-processing methods alone. Since the algorithm derives signal
from the change in OCT data, even small shifts in gaze or saccadic motion
of the eye could result in significant artifacts. Post-processing methods
to correct for transverse motion artifacts have limited success and are
often very time consuming. One of the approaches to solve this problem is
to use very high speed OCT systems, however, such systems can be very
complex and costly (see for example T. Klein et al., "The effect of
micro-saccades on the image quality of ultrawide-field multimegahertz OCT
data," SPIE Photonices West 2012, Paper # 8209-13 (2012)).

[0007] Another challenge for the OCT angiography technology is to obtain
retinal vasculature maps at large fields of view (FOV). The large
acquisition times and huge data volumes make it impractical to obtain
high resolution data over large FOVs. Acquisition of multiple smaller
data cubes of smaller FOV and montaging them together using
post-processing is one of the approaches that can be applied to work
around this problem. Rosenfeld et al. recently demonstrated a method for
automated montaging of SD-OCT data sets to generate images and analysis
over larger FOV (see for example Y. Li et al., "Automatic montage of
SD-OCT data sets,", Optics Express, 19, 26239-26248 (2011)). However,
their method relies on post-processing registration and alignment of
multiple OCT cubes based on their OCT-fundus images. There are several
limitations in this method. Sufficient overlap of the scanned data is
required for optimized performance of the algorithms and it must be
ensures that changes in gaze do not result in missing un-scanned regions
on the retina. Also, if there is some motion during the scan, it cannot
be corrected using this method.

[0008] In light of the limitations in the prior art, a need exists to
obtain motion artifact free OCT angiography images, especially large
field of view images.

SUMMARY

[0009] In this invention, we describe and demonstrate a tracking based
approach to generate reduced motion-artifact functional OCT data.
Multiple OCT measurements at a given sample location can be analyzed to
ascertain structural or functional changes over varying time scales.
Either OCT intensity or phase-resolved OCT data can be used for such data
analysis methods. OCT angiography is one such example where inter-frame
analysis can be used to detect blood-flow by using motion-contrast.
High-resolution OCT angiography requires long acquisition times and hence
the final results are highly susceptible to errors caused by subject
motion. En face vasculature images obtained by OCT angiography often
contain horizontal stripe artifacts due to uncompensated lateral motion.
Here we propose a method, wherein two or more OCT A-scans are obtained at
the same location while the eye position is being monitored using
tracking methods. With the use of eye tracking information, it is ensured
that at least two or more A-scans are obtained from the same tissue
location, and the difference between the two A-scans is calculated and
analyzed to ascertain structural or functional changes accurately without
any eye motion related artifacts. Retinal tracking information can also
be used to guide acquisition of multiple cube scans with fixed offsets to
create a large field-of-view (FOV) composite or montaged image. The use
of retinal tracking can significantly reduce the post-processing efforts
in order to create a large FOV analysis by guided montaging of smaller
FOV scans during data acquisition.

[0010] In one embodiment of the invention, the repeated acquisitions
required to generate contrast data are considered as a single unit or
block of data that we will refer to as a `cluster scan`. In this
embodiment of the invention, the acquisition of single or integer
multiples of cluster scan units is synchronized with the motion tracking
update rate in order to reduce the motion artifact effects on the dynamic
structural or functional change analysis of OCT data. When multiple OCT
measurements are used to measure rapidly changing structural or
functional information, it is imperative that all the OCT measurements
within the cluster scan are obtained within a short time window to enable
high resolution, precise and accurate change analysis.

[0011] One exemplary example for an application of this invention is OCT
angiography, where the blood flow results in changes within the order of
few milliseconds. The majority of the methods for OCT angiography acquire
multiple B-scans or frames (say N repeat B-scans at the same location or
closely spaced) and analyze the change in complex or intensity-only OCT
data between B-scans (referred to as inter-frame analysis) due to motion.
The idea being to separate scattering data due to motion from scattering
data due to static elements being imaged. Hence in this case, the set of
N B-scans can be considered as a cluster scan and an image based retinal
tracking system can be adapted to synchronize the update rate of image
frames with the time taken to acquire a cluster scan data. The
synchronization of the update rate for retinal tracking algorithm with
the cluster scan acquisition rate will ensure that:

[0012] 1. Data
acquired during an event of transverse motion is not used for motion
contrast or change analysis.

[0013] 2. Any cluster containing complete or
partial data obtained during motion is rejected and the cluster scan is
repeated after motion correction and eye stabilization.

[0014] Additionally, the instrument user has the capability to adjust the
motion tolerance parameter in order to enable obtaining the data in an
efficient way in the shortest possible time. We have demonstrated the
implementation of the above mentioned solution and significant
improvement in the OCT vasculature image quality was observed.

BRIEF DESCRIPTION OF THE FIGURES

[0015] FIG. 1 is a diagram of a generalized OCT system.

[0016] FIG. 2 shows an en face image of the retina generated from OCT data
and illustrates the impact motion can have on these types of images.

[0017] FIG. 3 shows a diagram of a combined OCT scanner and a line-scan
ophthalmoscope (LSO).

[0019] FIG. 5 shows a block diagram of a preferred tracking system for use
with the present invention.

[0020] FIG. 6 illustrates a series of steps that could be used to generate
a montaged en face vasculature image according to the present invention.

DETAILED DESCRIPTION

[0021] A diagram of a generalized OCT system is shown in FIG. 1. Light
from source 101 is routed, typically by optical fiber 105, to illuminate
the sample 110, a typical sample being tissues in the human eye. The
source 101 can be either a broadband light source with short temporal
coherence length in the case of SD-OCT or a wavelength tunable laser
source in the case of SS-OCT. The light is scanned, typically with a
scanner 107 between the output of the fiber and the sample, so that the
beam of light (dashed line 108) is scanned laterally (in x and y) over
the area or volume to be imaged. Light scattered from the sample is
collected, typically into the same fiber 105 used to route the light for
sample illumination. Reference light derived from the same source 101
travels a separate path, in this case involving fiber 103 and
retro-reflector 104 with an adjustable optical delay. Those skilled in
the art recognize that a transmissive reference path can also be used and
that the adjustable delay could be placed in the sample or reference arm
of the interferometer. Collected sample light is combined with reference
light, typically in a fiber coupler 102, to form light interference in a
detector 120. Although a single fiber port is shown going to the
detector, those skilled in the art recognize that various designs of
interferometers can be used for balanced or unbalanced detection of the
interference signal. The output from the detector is supplied to a
processor 121. The results can be stored in the processor 121 or
displayed on display 122. The processing and storing functions may be
localized within the OCT instrument or functions may be performed on an
external processing unit to which the collected data is transferred. This
unit could be dedicated to data processing or perform other tasks which
are quite general and not dedicated to the OCT device.

[0022] The sample and reference arms in the interferometer could consist
of bulk-optics, fiber-optics or hybrid bulk-optic systems and could have
different architectures such as Michelson, Mach-Zehnder or common-path
based designs as would be known by those skilled in the art. Light beam
as used herein should be interpreted as any carefully directed light
path. In time-domain systems, the reference arm needs to have a tunable
optical delay to generate interference. Balanced detection systems are
typically used in TD-OCT and SS-OCT systems, while spectrometers are used
at the detection port for SD-OCT systems. The invention described herein
could be applied to anytime of OCT system capable of generating data for
functional analysis.

[0023] The interference causes the intensity of the interfered light to
vary across the spectrum. The Fourier transform of the interference light
reveals the profile of scattering intensities at different path lengths,
and therefore scattering as a function of depth (z-direction) in the
sample (see for example Leitgeb et al. "Ultrahigh resolution Fourier
domain optical coherence tomography," Optics Express 12(10):2156 (2004)).
The profile of scattering as a function of depth is called an axial scan
(A-scan). A set of A-scans measured at neighboring locations in the
sample produces a cross-sectional image (tomogram or B-scan) of the
sample. A collection of B-scans collected at different transverse
locations on the sample makes up a data volume or cube. For a particular
volume of data, the term fast axis refers to the scan direction along a
single B-scan whereas slow axis refers to the axis along which multiple
B-scans are collected. We use the term "cluster scan" herein to refer to
a single unit or block of data generated by repeated acquisitions at the
same location for the purposes of analyzing motion contrast. A cluster
scan can consist of multiple a-scans or B-scans collected over time at a
single location. A variety of ways to create B-scans are known to those
skilled in the art including but not limited to along the horizontal or
x-direction, along the vertical or y-direction, along the diagonal of x
and y, or in a circular or spiral pattern. The majority of the examples
discussed herein refer to B-scans in the x-z dimensions but the invention
would apply equally to any cross sectional image.

[0024] In Functional OCT, differences between data collected at the same
location at different times are used to analyze motion or flow. An en
face vasculature image is an image displaying motion contrast signal in
which the data dimension corresponding to depth is displayed as a single
representative value, typically by summing or integrating an isolated
portion of the data. For generating the enface images described herein,
each B-scan in the given data volume consists of 300 A-scans, each
cluster scan consists of four B-scans, for a total of eighty different
cluster scans. Hence, the number of A-scans in a given unit data volume
are 300×80×4. After processing the data to highlight motion
contrast using any one of the known motion contrast techniques, a range
of 25-30 pixels corresponding to 50-60 microns of tissue depth from the
surface of internal limiting membrane (ILM) in retina, are summed to
generate an en face image of the vasculature. Each B-scan takes
approximately 12 ms to acquire (including fly-back time) so the time
between B-scans is approximately 12 ms which is on the order of interest
for retinal vasculature dynamics. For the enface image shown in FIG. 2,
three volumes of data were collected with some overlapping area in the
retina. The enface images obtained from the three volumes were montaged
or combined to create a larger field of view enface image.

[0025] For large data volume acquisitions, such as those required for
motion contrast analysis, the possibility and occurrences of eye motion
increases. Eye motion can result in loss of data and image artifacts,
hence greatly reducing the usability of the acquired data. In the time
(usually a few seconds) required to build a useful map of vasculature,
the patient's gaze can shift, causing the retinal image to move from the
point of view of the ophthalmic device. In the image displayed in FIG. 2,
generated from data taken without any motion tracking and without any
motion correction processing, there are two kinds of motion artifacts
caused due to transverse eye motion that are clearly visible:

[0026] 1.
Horizontal line artifacts in the en face vasculature image of retina
caused by small or transient transverse shifts of fixation of the eye
(arrow 201)

[0027] 2. Appearance of shifted blocks of data within a
single cube of data caused by small changes in the fixation of the eye
(arrow 202)

[0028] FIG. 4 was generated by collecting data according to the process
outlined above. Three sets of data are collected and processed separately
and montaged together in post processing to generate the en face image.
The three data sets are separated by thin horizontal lines 203.

[0029] Here we describe two approaches to solve the problem caused by eye
motion in OCT angiography data collection that can be incorporated into
OCT systems to enable higher quality and larger field of view motion
contrast images. The first is a post processing based approach in which
motion correction techniques are applied to data for inter-frame analysis
comprising the following steps:

[0030] a. Register the set of B-scans
within a cluster scan to account for shifts in fixation.

[0031] b.
Calculate the inter-frame motion-contrast information for each cluster,
but only for the B-scan regions having overlap. Discard the portions of
the B-scan or even entire B-scans if there is no overlap.

[0032] c.
Calculate the shifts caused by changes in eye fixation and adjust the
displacement of subsequent clusters or data blocks for motion corrected
visualization of enface vasculature images.

[0033] Describing this approach in further detail, volumes of OCT data are
collected with each volume consisting of a plurality of cluster scans
taken at the same location. After data collection, a processor is used to
compare the B-scans in each cluster by autocorrelation or some other
registration technique known by those skilled in the art to identify
matching portions. If portions are identified with differences exceeding
predefined criteria, those portions can be excluded from further
analysis. This could result in full B-scans or clusters being excluded.
Any one of a variety of motion contrast techniques (phase contrast,
Doppler variance, OMAG, etc) can then be applied to the matched B-scans
to determine the motion contrast within the sample during the collection
of the data. If a loss of fixation was found to extend beyond the time
required to acquire a single cluster, the amount of the motion shift can
be determined and used to shift subsequent clusters in a data volume.

[0034] While post processing methods may be helpful, there are however, a
few limitations of this method. Firstly, this post-processing effort
could be very time consuming and intensive. Secondly, while post
processing based registration can correct for motion along the fast scan
axis, it will not be effective if the motion happens along the slow scan
direction.

[0035] The second and preferred approach involves the use of retinal
tracking during scan acquisition. Retinal tracking can be very useful to
remove subject motion artifacts for motion-contrast OCT imaging. Retinal
tracking can be used to acquire two or more OCT A-scans from the same
location while simultaneously monitoring the eye position. Hence
differences between at least two of these scans can be measured
accurately to determine motion contrast, as tracking ensures that the
scans are acquired from the same position. The invention described herein
can apply to any tracking system capable of detecting motion of the eye.
As will be discussed in further detail below, there are several known
mechanisms for retinal tracking during OCT data acquisition such as use
of a fundus imaging modality (CSLO, SLO etc.) or use of OCT itself to
correct for motion.

[0036] A specific tracked OCT data collection system combining an OCT
scanner and a line-scan ophthalmoscope (LSO) is described in U.S. Pat.
No. 7,805,009 hereby incorporated by reference and illustrated in FIG. 3.
In this system, light from the LSO light source 301 is routed by
cylindrical lens 302 and beamsplitter 303 to scanning minor 304. The
cylindrical lens 302 and the scan lens 305 produce a line of illumination
at the retinal image plane 306, and the ocular lens 307 and optics of the
human eye 300 re-image this line of illumination onto the retina 310. The
line of illumination is swept across the retina as the scanning mirror
304 rotates. Reflected light from the retina approximately reverses the
path of the LSO illumination light; the reflected light is scanned by the
LSO scan mirror 304 so that the illuminated portion of the retina is
continuously imaged by imaging lens 308 onto the LSO line camera 309. The
LSO line camera converts the reflected LSO light into a data stream
representing single-line partial images, which can be processed to form
both eye tracking in formation and a real-time display of the retina.

[0037] The OCT system 320 incorporates the light source, light detector or
detectors, interferometer and processor required to determine the depth
profile of backscattered light from the OCT beam 321 as illustrated and
described for FIG. 1. OCT scanner 322 sweeps the angle of the OCT beam
laterally across the surface in two dimensions (x and y), under the
control of scan controller 354. Scan lens 323 brings the OCT beam into
focus on the retinal image plane 306. Beamsplitter 324 combines the OCT
and LSO beam paths so that both paths can more easily be directed through
the pupil of the human eye 300. (Combining the beam paths is not required
in direct imaging applications, where the object itself lies in the
location of the retinal image plane 306.) If the OCT and LSO use
different wavelengths of light, beamsplitter 324 can be implemented as a
dichroic minor. The OCT beam is re-focused onto the retina through ocular
lens 307 and the optics of the human eye 300. Some light scattered from
the retina follows the reverse path of the OCT beam and returns to the
OCT system 320, which determines the amount of scattered light as a
function of depth along the OCT beam.

[0038] In this case, the LSO image is used to provide feedback to the OCT
system while collecting data for functional OCT analysis. It is critical
to maintain the precise location for repeat measurements because small
displacements between multiple repeat measurements obtained for change
analysis can give erroneous results. While a series of A or B-scans are
collected, the LSO image stream can be used to maintain a specific
location on the retina so that when the data is processed with a motion
contrast technique, differences are calculated between scans taken at the
same location so that the highest quality image can be obtained. Tracking
further enables precise positioning of multiple cluster scans so that
cubes or volumes of data can be collected with precise location
information minimizing the post-processing time and effort to generate
final images as will be described in further detail below. In this
embodiment the LSO image update rate could be arbitrary as long as the
tracking mechanism ensures that at least two measurements performed for
the change analysis are obtained from the same spatial location.

[0039] In a further embodiment of the invention using retinal tracking,
the simultaneous LSO based retinal tracking can be adapted to synchronize
the update rate of LSO frames with the time taken to acquire a single
cluster scan. This will ensure that the temporal spacing between the
multiple measurements within the cluster scan is uniform, resulting in
more accurate calculation of changes in signal.

[0040] To illustrate the concept, here we define several parameters for
each imaging modality for their respective scans:

[0041] 1. Fundus
Imaging Generation Period (TFI): The time period required to
generate one fundus image.

[0042] 2. Fundus Imaging Duty Cycle
(FDC): The fundus imaging modality may not operate at 100% duty
cycle and hence the effective fundus imaging update time is given by:
TFI/FDC.

[0043] 3. OCT Cluster Data Acquisition Time
(TCLUSTER): The time period required for the OCT imaging system to
finish acquisition of a single cluster scan comprising of a given number
of repeated B-scans at the same or closely spaced locations. This time
also includes the flyback times, and settling times for the scanners.

[0044] 4. Synchronization Condition: The effective fundus imaging update
time (TFI/FDC) should be equal to an integral multiple of the
OCT Cluster Data Acquisition Time (TCLUSTER):

[0044] TFI=FDCTCLUSTER

[0045] The fundus imaging duty cycle can be adjusted to the above
condition. Another alternative could be to have multiple fundus imaging
updates during the acquisition of a single cluster, but ensuring that the
entire cluster is discarded and scanned again if the motion happened
during the acquisition of the cluster. A more generalized synchronization
condition would be:

T F I = ( M N ) F D C T CLUSTER ,
##EQU00001##

where M and N are integer numbers.

[0046] Once the synchronization condition is satisfied, the retinal
tracking based acquisition would result in motion artifact free data
acquisition for OCT angiography by providing the ability to analyze and
reject data on a cluster by cluster basis. An image generated using this
technique is shown in FIG. 4. A composite scan pattern consisting of
three data cubes in the vertical direction (3×1) was used to
generate the enface vasculature image in FIG. 4. Each cube has 80 sets of
4 cluster scans with each B-scan in the cluster scans having 300 A-scans
(300×80×4). Motion artifacts such as horizontal line
artifacts and shifted blocks of OCT data shown in FIG. 2 have been
corrected by use of retinal tracking. The enface image from each cube was
montaged automatically using an autocorrelation based approach. The
tracking system utilized had an accuracy of approximately 50 microns. The
middle data cube was shifted laterally by approximately 50 microns to
achieve the best match.

[0047] While the embodiment above describes a tracking mechanism in which
a second imaging modality is used to monitor the eye position for
possible motion, the scope of this invention is not limited to any
specific tracking method and an OCT measurement could provide the basis
of the tracking. A central idea of this invention is the use of tracking
methods to obtain repeat measurements at the same location in order to
generate high quality functional contrast in OCT images.

[0048] Several variants of retina tracking have been proposed and are used
to follow and correct for eye motion, and hence can be applied to this
invention. For example, systems have been described that detect apparent
motion of the retina using a tracking beam and move minors in the imaging
path to provide a stabilized OCT image (see for example U.S. Pat. Nos.
6,736,508, 6,726,325 and 6,325,512). U.S. Pat. No. 7,805,009 as discussed
above describes the use of a line scan ophthalmoscope to monitor the
position of the eye and apply a correction to the OCT scanner. Even with
tracking or registration, there are however, situations that cause some
of the measurement data to be unusable. The methods described above do
not address the problem of missing data caused by events such as blinking
of eye and rapid shifts of gaze in a saccadic motion. The method
described in U.S. patent application Ser. No. 13/433,127 filed Mar. 28,
2012 hereby incorporated by reference, overcomes one or more of the
above-identified limitations. The system includes the following elements
as illustrated in FIG. 5:

[0049] 1. A measurement system 501 for
acquiring ophthalmic measurements.

[0050] 2. An imaging system 502 (LSO,
cSLO, etc.) that produces images of the retina to be used by the tracking
system to analyze motion.

[0051] 3. A synchronization mechanism between
(1) and (2).

[0052] 4. A quality monitoring system 503 that analyzes the
images of the eye to decide if they are of sufficient quality for
tracking purposes, additionally this system helps to select the best
image to be used as a reference image for the tracking system described
next.

[0053] 5. A retinal tracking system 504 capable of determining if
the retina has moved based on a comparison to a reference image or frame.
The retinal tracking system can detect motion in any or all of the x,y,
and z dimensions.

[0054] 6. A decision system 505 that decides based on
the input from the retinal tracking system 504, the measurement system
501, and some other pre-defined criteria whether the acquired data is
acceptable. If it is acceptable, the measurement data is stored in
memory. If it is not, it instructs the measurement system to go back and
rescan the data optionally with computed offsets to compensate for
motion.

[0055] 7. A user interface system 506 that displays relevant
information to the user and gets inputs from the user for the different
systems as needed.

[0056] A key aspect of this tracking method is the decision system 505. It
provides the ability to determine when to go back and re-scan the
measurement data based on different inputs to the system or when to
continue on with data collection if the scan has exceeded a predetermined
amount of time. This is important for motion contrast imaging as it is
desirable to collect the multiple scans in a cluster scan in a restricted
amount of time with ideally even spacings between the multiple scans.

[0057] In our preferred embodiment of the present invention for generation
of flow-contrast images, N repeated measurements are taken at the same
location with the tracking method described above that is capable of
re-scanning or continuing data collection based on the decision system.
The system could be designed with preset criteria or allow the user to
input criteria that will collect high quality functional OCT data. In
using this system, it is ensured that:

[0058] 1. Data acquired during
an event of bulk transverse motion is not used for motion contrast
analysis.

[0059] 2. Any cluster containing complete or partial data
obtained during motion is rejected and the location is re-scanned after
motion correction.

[0060] 3. The user has the capability to adjust the
motion tolerance parameter in order to enable the collection of data in
an efficient way in the shortest possible time.

[0061] Retinal Tracking Based Composite Scan Patterns to Obtain Large
Field of View Images

[0062] In another embodiment of this invention, we propose using retinal
tracking for generating multiple scans with fixed offsets to create a
large field-of-view (FOV) composite or montaged image using several en
face images of vasculature in retina. Li et al. recently demonstrated a
method for automated montaging of SD-OCT data sets to generate images and
analysis over larger FOV (see for example Y. Li et al., "Automatic
montage of SD-OCT data sets,", Optics Express, 19, 26239-26248 (2011)).
However, their method relies on post-processing registration and
alignment of multiple OCT cubes based on their OCT-fundus images. The
multiple OCT cubes were acquired with small overlaps and the montaging
was done for the full 3-D volume. However, there are several limitations
in this method. Sufficient overlap of the scanned data is required for
optimized performance of the algorithms and to ensure that changes in
gaze does not result in missing un-scanned regions on the retina. Also,
if there is some motion during the scan, it cannot be corrected by this
method.

[0063] In contrast, the method described herein relies on tracking based
information to decide the placement of multiple small FOV scan patterns
with contiguous boundaries. Sophisticated registration or montaging
algorithms are not required because the tracking information is used to
dynamically correct for eye motion, tilt and angle changes during
multiple scan cubes. For example, retinal tracking can enable adjustment
of starting spatial coordinates with respect to the reference point on
the retinal surface for a scan pattern with a given geometry. Pre-defined
spatial positions can be selected as the starting point for a given scan
volume with fixed dimensions such that multiple data volumes can be
combined together with an adjustable level of overlap at the boundary.
FIG. 4 shows the enface image obtained by a tracking enabled composite
scan pattern (3×1). The enface image from each cube was montaged
automatically using an autocorrelation based approach. It is clear from
the montage image that retinal tracking helps and corrects for eye motion
even for extended period scans. FIG. 6 shows the workflow for the
acquisition of the given composite scan pattern (3×1) for
collection of OCT angiography data. After an initial alignment of the
patient and scan type selection, each data cube is collected and the
patient is allowed to sit back from the instrument and relax between the
long scans because the system is capable of recognizing where the last
scan was taken and positioning the next scan accordingly. This scan
acquisition and analysis pattern could be accomplished by a single
"click", button press, or other type of interaction with the user
interface of the device.

[0064] Retinal tracking based montaging of multiple en face images
generated from multiple phase-contrast data sets has several advantages.
A priori knowledge of the spatial co-ordinates of the en face images
makes it easier to stitch multiple images. For example, the data
acquisition times for an image with FOV of 3 mm×1.2 mm can be
longer than that of the standard cube data sets with FOV of 6 mm×6
mm. Hence it is desirable to be able to obtain multiple OCT angiography
data sets that can be automatically stitched together to provide a larger
FOV image without any sophisticated post-processing.

[0065] Although various embodiments that incorporate the teachings of the
present invention have been shown and described in detail herein, those
skilled in the art can readily devise many other varied embodiments that
still incorporate these teachings and may not require all of the above
described elements to fall within the scope of the invention. While the
descriptions have focused on retinal OCT angiography using an image based
retinal tracking system, the basic concepts could be applied to any
functional OCT imaging modality and motion tracking system.